Problem
Research questions and friction points this paper is trying to address.
AI-generated video detection
unnatural artifacts
reliable detection
TUE-Detector: A Tool-Using Expert MLLM-Based Detector for AI-Generated Videos
🤖 AI Summary
本文针对AI生成视频检测中的细微不自然痕迹识别难题，提出基于工具使用专家MLLM的TUE-Detector框架，通过调用适当工具收集并分析证据，以实现可靠检测。
📝 Abstract
AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perform this task reliably, a key challenge lies in accurately identifying subtle-yet-measurable unnatural artifacts. In this work, we address this challenge from a novel perspective of tool-mediated evidence discovery and propose Tool-Using Expert MLLM-based AI-generated Video Detector (TUE-Detector), a novel framework for AI-generated video detection. TUE-Detector trains a general MLLM into a task-tailored tool-using expert detector that learns to invoke suitable tools, collect concrete evidence of unnaturalness, and reason over the evidence for reliable detection. Meanwhile, TUE-Detector further introduces novel designs to equip the expert detector with high-quality and suitable tools. Extensive experiments demonstrate the effectiveness of our framework.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Tool-Using Expert
MLLM-based Detector
AI-generated Video Detection
Evidence Discovery
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